Overview

Dataset statistics

Number of variables15
Number of observations6736
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory789.5 KiB
Average record size in memory120.0 B

Variable types

Numeric10
Categorical5

Alerts

TIME is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with TIME and 13 other fieldsHigh correlation
T1 is highly correlated with TIME and 13 other fieldsHigh correlation
T4 is highly correlated with TIME and 13 other fieldsHigh correlation
T5 is highly correlated with TIME and 13 other fieldsHigh correlation
T9 is highly correlated with TIME and 13 other fieldsHigh correlation
T10 is highly correlated with TIME and 13 other fieldsHigh correlation
T11 is highly correlated with TIME and 13 other fieldsHigh correlation
T12 is highly correlated with TIME and 13 other fieldsHigh correlation
Z is highly correlated with TIME and 13 other fieldsHigh correlation
T2 is highly correlated with TIME and 13 other fieldsHigh correlation
T3 is highly correlated with TIME and 11 other fieldsHigh correlation
T6 is highly correlated with TIME and 11 other fieldsHigh correlation
T7 is highly correlated with TIME and 11 other fieldsHigh correlation
T8 is highly correlated with TIME and 11 other fieldsHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
S has 728 (10.8%) zeros Zeros
Z has 80 (1.2%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:28:23.353863
Analysis finished2022-11-11 03:28:29.432437
Duration6.08 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct6736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.625
Minimum0
Maximum561.25
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:29.459154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.0625
Q1140.3125
median280.625
Q3420.9375
95-th percentile533.1875
Maximum561.25
Range561.25
Interquartile range (IQR)280.625

Descriptive statistics

Standard deviation162.0550032
Coefficient of variation (CV)0.5774788534
Kurtosis-1.2
Mean280.625
Median Absolute Deviation (MAD)140.3333333
Skewness-1.33126377 × 10-16
Sum1890290
Variance26261.82407
MonotonicityStrictly increasing
2022-11-11T11:28:29.514207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
372.83333331
 
< 0.1%
374.83333331
 
< 0.1%
374.751
 
< 0.1%
374.66666671
 
< 0.1%
374.58333331
 
< 0.1%
374.51
 
< 0.1%
374.41666671
 
< 0.1%
374.33333331
 
< 0.1%
374.251
 
< 0.1%
Other values (6726)6726
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
561.251
< 0.1%
561.16666671
< 0.1%
561.08333331
< 0.1%
5611
< 0.1%
560.91666671
< 0.1%
560.83333331
< 0.1%
560.751
< 0.1%
560.66666671
< 0.1%
560.58333331
< 0.1%
560.51
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11055.98352
Minimum0
Maximum18000
Zeros728
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:29.563608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18000
median12001
Q312001
95-th percentile18000
Maximum18000
Range18000
Interquartile range (IQR)4001

Descriptive statistics

Standard deviation5092.430336
Coefficient of variation (CV)0.4606040093
Kurtosis0.1699434198
Mean11055.98352
Median Absolute Deviation (MAD)4001
Skewness-0.6083629808
Sum74473105
Variance25932846.73
MonotonicityNot monotonic
2022-11-11T11:28:29.601416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
120012877
42.7%
180001412
21.0%
80001136
 
16.9%
0728
 
10.8%
7999302
 
4.5%
9998253
 
3.8%
1799827
 
0.4%
110901
 
< 0.1%
ValueCountFrequency (%)
0728
 
10.8%
7999302
 
4.5%
80001136
 
16.9%
9998253
 
3.8%
110901
 
< 0.1%
120012877
42.7%
1799827
 
0.4%
180001412
21.0%
ValueCountFrequency (%)
180001412
21.0%
1799827
 
0.4%
120012877
42.7%
110901
 
< 0.1%
9998253
 
3.8%
80001136
 
16.9%
7999302
 
4.5%
0728
 
10.8%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.84732779
Minimum24.8
Maximum26.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:29.652539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.8
5-th percentile24.8
Q125.5
median25.8
Q325.9
95-th percentile26.8
Maximum26.8
Range2
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.5819726713
Coefficient of variation (CV)0.0225157771
Kurtosis-0.4272213702
Mean25.84732779
Median Absolute Deviation (MAD)0.3
Skewness0.2617425871
Sum174107.6
Variance0.3386921901
MonotonicityNot monotonic
2022-11-11T11:28:29.707360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
25.51434
21.3%
25.91417
21.0%
26.81366
20.3%
25.81183
17.6%
24.8552
 
8.2%
25.7244
 
3.6%
24.9118
 
1.8%
25.6112
 
1.7%
2567
 
1.0%
25.437
 
0.5%
Other values (31)206
 
3.1%
ValueCountFrequency (%)
24.8552
8.2%
24.852
 
< 0.1%
24.9118
 
1.8%
24.952
 
< 0.1%
2567
 
1.0%
25.051
 
< 0.1%
25.126
 
0.4%
25.151
 
< 0.1%
25.222
 
0.3%
25.251
 
< 0.1%
ValueCountFrequency (%)
26.81366
20.3%
26.752
 
< 0.1%
26.728
 
0.4%
26.652
 
< 0.1%
26.620
 
0.3%
26.552
 
< 0.1%
26.57
 
0.1%
26.452
 
< 0.1%
26.45
 
0.1%
26.353
 
< 0.1%

T2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
24.8
3823 
24.7
1527 
24.6
1386 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters26944
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.8
2nd row24.8
3rd row24.8
4th row24.8
5th row24.8

Common Values

ValueCountFrequency (%)
24.83823
56.8%
24.71527
 
22.7%
24.61386
 
20.6%

Length

2022-11-11T11:28:29.834825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:28:29.880886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.83823
56.8%
24.71527
 
22.7%
24.61386
 
20.6%

Most occurring characters

ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
83823
14.2%
71527
 
5.7%
61386
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20208
75.0%
Other Punctuation6736
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
26736
33.3%
46736
33.3%
83823
18.9%
71527
 
7.6%
61386
 
6.9%
Other Punctuation
ValueCountFrequency (%)
.6736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
83823
14.2%
71527
 
5.7%
61386
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
83823
14.2%
71527
 
5.7%
61386
 
5.1%

T3
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
24.7
3064 
24.8
2228 
24.5
1374 
24.6
 
70

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters26944
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.8
2nd row24.8
3rd row24.8
4th row24.8
5th row24.8

Common Values

ValueCountFrequency (%)
24.73064
45.5%
24.82228
33.1%
24.51374
20.4%
24.670
 
1.0%

Length

2022-11-11T11:28:29.921871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:28:29.968712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.73064
45.5%
24.82228
33.1%
24.51374
20.4%
24.670
 
1.0%

Most occurring characters

ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
73064
11.4%
82228
 
8.3%
51374
 
5.1%
670
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20208
75.0%
Other Punctuation6736
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
26736
33.3%
46736
33.3%
73064
15.2%
82228
 
11.0%
51374
 
6.8%
670
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.6736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
73064
11.4%
82228
 
8.3%
51374
 
5.1%
670
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
73064
11.4%
82228
 
8.3%
51374
 
5.1%
670
 
0.3%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.11469715
Minimum24.9
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:30.007504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.9
5-th percentile24.9
Q125
median25.1
Q325.1
95-th percentile25.5
Maximum25.5
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1829093358
Coefficient of variation (CV)0.007282960041
Kurtosis-0.2383397748
Mean25.11469715
Median Absolute Deviation (MAD)0.1
Skewness1.005919754
Sum169172.6
Variance0.03345582512
MonotonicityNot monotonic
2022-11-11T11:28:30.043384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
252485
36.9%
25.11816
27.0%
24.9793
 
11.8%
25.5654
 
9.7%
25.4599
 
8.9%
25.3259
 
3.8%
25.2130
 
1.9%
ValueCountFrequency (%)
24.9793
 
11.8%
252485
36.9%
25.11816
27.0%
25.2130
 
1.9%
25.3259
 
3.8%
25.4599
 
8.9%
25.5654
 
9.7%
ValueCountFrequency (%)
25.5654
 
9.7%
25.4599
 
8.9%
25.3259
 
3.8%
25.2130
 
1.9%
25.11816
27.0%
252485
36.9%
24.9793
 
11.8%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.0959323
Minimum25
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:30.083783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25
Q125
median25
Q325.1
95-th percentile25.4
Maximum25.5
Range0.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1465102823
Coefficient of variation (CV)0.005838009146
Kurtosis0.3107204832
Mean25.0959323
Median Absolute Deviation (MAD)0
Skewness1.34325014
Sum169046.2
Variance0.02146526283
MonotonicityNot monotonic
2022-11-11T11:28:30.125593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
254046
60.1%
25.11152
 
17.1%
25.4863
 
12.8%
25.2319
 
4.7%
25.3280
 
4.2%
25.576
 
1.1%
ValueCountFrequency (%)
254046
60.1%
25.11152
 
17.1%
25.2319
 
4.7%
25.3280
 
4.2%
25.4863
 
12.8%
25.576
 
1.1%
ValueCountFrequency (%)
25.576
 
1.1%
25.4863
 
12.8%
25.3280
 
4.2%
25.2319
 
4.7%
25.11152
 
17.1%
254046
60.1%

T6
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
25.0
3225 
25.1
2904 
25.2
607 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters26944
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25.2
2nd row25.2
3rd row25.2
4th row25.2
5th row25.2

Common Values

ValueCountFrequency (%)
25.03225
47.9%
25.12904
43.1%
25.2607
 
9.0%

Length

2022-11-11T11:28:30.170470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:28:30.217251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
25.03225
47.9%
25.12904
43.1%
25.2607
 
9.0%

Most occurring characters

ValueCountFrequency (%)
27343
27.3%
56736
25.0%
.6736
25.0%
03225
12.0%
12904
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20208
75.0%
Other Punctuation6736
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
27343
36.3%
56736
33.3%
03225
16.0%
12904
 
14.4%
Other Punctuation
ValueCountFrequency (%)
.6736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
27343
27.3%
56736
25.0%
.6736
25.0%
03225
12.0%
12904
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII26944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27343
27.3%
56736
25.0%
.6736
25.0%
03225
12.0%
12904
 
10.8%

T7
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
24.6
3061 
24.7
2212 
24.4
1379 
24.5
 
84

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters26944
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.7
2nd row24.7
3rd row24.7
4th row24.7
5th row24.7

Common Values

ValueCountFrequency (%)
24.63061
45.4%
24.72212
32.8%
24.41379
20.5%
24.584
 
1.2%

Length

2022-11-11T11:28:30.258393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:28:30.305496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.63061
45.4%
24.72212
32.8%
24.41379
20.5%
24.584
 
1.2%

Most occurring characters

ValueCountFrequency (%)
48115
30.1%
26736
25.0%
.6736
25.0%
63061
 
11.4%
72212
 
8.2%
584
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20208
75.0%
Other Punctuation6736
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
48115
40.2%
26736
33.3%
63061
 
15.1%
72212
 
10.9%
584
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.6736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
48115
30.1%
26736
25.0%
.6736
25.0%
63061
 
11.4%
72212
 
8.2%
584
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48115
30.1%
26736
25.0%
.6736
25.0%
63061
 
11.4%
72212
 
8.2%
584
 
0.3%

T8
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
24.8
5750 
24.7
834 
24.9
 
152

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters26944
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.8
2nd row24.8
3rd row24.8
4th row24.8
5th row24.8

Common Values

ValueCountFrequency (%)
24.85750
85.4%
24.7834
 
12.4%
24.9152
 
2.3%

Length

2022-11-11T11:28:30.349195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:28:30.394046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.85750
85.4%
24.7834
 
12.4%
24.9152
 
2.3%

Most occurring characters

ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
85750
21.3%
7834
 
3.1%
9152
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20208
75.0%
Other Punctuation6736
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
26736
33.3%
46736
33.3%
85750
28.5%
7834
 
4.1%
9152
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.6736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
85750
21.3%
7834
 
3.1%
9152
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII26944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26736
25.0%
46736
25.0%
.6736
25.0%
85750
21.3%
7834
 
3.1%
9152
 
0.6%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct71
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.92447855
Minimum25.055
Maximum28.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:30.440888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.055
5-th percentile25.685
Q126.105
median26.735
Q327.89
95-th percentile28.31
Maximum28.73
Range3.675
Interquartile range (IQR)1.785

Descriptive statistics

Standard deviation0.9347115824
Coefficient of variation (CV)0.03471605145
Kurtosis-1.40216905
Mean26.92447855
Median Absolute Deviation (MAD)0.84
Skewness0.1244095758
Sum181363.2875
Variance0.8736857423
MonotonicityNot monotonic
2022-11-11T11:28:30.496706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.89430
 
6.4%
26416
 
6.2%
27.995352
 
5.2%
26.105345
 
5.1%
25.79343
 
5.1%
26.21299
 
4.4%
26.525298
 
4.4%
25.895295
 
4.4%
27.785290
 
4.3%
28.1278
 
4.1%
Other values (61)3390
50.3%
ValueCountFrequency (%)
25.05537
0.5%
25.10751
 
< 0.1%
25.1652
0.8%
25.21251
 
< 0.1%
25.2653
 
< 0.1%
25.31751
 
< 0.1%
25.374
 
0.1%
25.42251
 
< 0.1%
25.47555
0.8%
25.52751
 
< 0.1%
ValueCountFrequency (%)
28.7329
 
0.4%
28.67754
 
0.1%
28.62520
 
0.3%
28.57254
 
0.1%
28.5264
1.0%
28.46758
 
0.1%
28.41595
1.4%
28.362510
 
0.1%
28.31143
2.1%
28.257514
 
0.2%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.60815766
Minimum23.9
Maximum25.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:30.548537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.9
5-th percentile24.215
Q124.32
median24.53
Q324.845
95-th percentile25.16
Maximum25.37
Range1.47
Interquartile range (IQR)0.525

Descriptive statistics

Standard deviation0.3272315283
Coefficient of variation (CV)0.01329768497
Kurtosis-0.823613354
Mean24.60815766
Median Absolute Deviation (MAD)0.21
Skewness0.3771913063
Sum165760.55
Variance0.1070804731
MonotonicityNot monotonic
2022-11-11T11:28:30.589706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
24.4251295
19.2%
24.215873
13.0%
24.32662
9.8%
24.53605
9.0%
24.635557
8.3%
24.95524
7.8%
24.845510
 
7.6%
24.74441
 
6.5%
25.055423
 
6.3%
25.16417
 
6.2%
Other values (5)429
 
6.4%
ValueCountFrequency (%)
23.945
 
0.7%
24.00560
 
0.9%
24.11109
 
1.6%
24.215873
13.0%
24.32662
9.8%
24.4251295
19.2%
24.53605
9.0%
24.635557
8.3%
24.74441
 
6.5%
24.845510
 
7.6%
ValueCountFrequency (%)
25.3771
 
1.1%
25.265144
 
2.1%
25.16417
 
6.2%
25.055423
 
6.3%
24.95524
7.8%
24.845510
 
7.6%
24.74441
 
6.5%
24.635557
8.3%
24.53605
9.0%
24.4251295
19.2%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.34796467
Minimum23.375
Maximum25.475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:30.638179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.375
5-th percentile23.585
Q123.795
median24.215
Q324.845
95-th percentile25.16
Maximum25.475
Range2.1
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation0.5610584566
Coefficient of variation (CV)0.02304334117
Kurtosis-1.407614376
Mean24.34796467
Median Absolute Deviation (MAD)0.525
Skewness0.1313485049
Sum164007.89
Variance0.3147865918
MonotonicityNot monotonic
2022-11-11T11:28:30.682995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
23.69725
10.8%
23.795596
 
8.8%
23.9572
 
8.5%
25.055559
 
8.3%
24.635514
 
7.6%
24.11473
 
7.0%
24.74457
 
6.8%
24.845379
 
5.6%
24.95375
 
5.6%
24.005330
 
4.9%
Other values (11)1756
26.1%
ValueCountFrequency (%)
23.37511
 
0.2%
23.48194
 
2.9%
23.585293
4.3%
23.69725
10.8%
23.795596
8.8%
23.9572
8.5%
24.005330
4.9%
24.11473
7.0%
24.215221
 
3.3%
24.32122
 
1.8%
ValueCountFrequency (%)
25.47513
 
0.2%
25.3792
 
1.4%
25.265199
 
3.0%
25.16308
4.6%
25.055559
8.3%
24.95375
5.6%
24.845379
5.6%
24.74457
6.8%
24.635514
7.6%
24.53222
 
3.3%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.28901128
Minimum23.48
Maximum25.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.8 KiB
2022-11-11T11:28:30.727844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.48
5-th percentile23.69
Q124.005
median24.215
Q324.635
95-th percentile24.95
Maximum25.16
Range1.68
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.422295775
Coefficient of variation (CV)0.01738628922
Kurtosis-1.07814048
Mean24.28901128
Median Absolute Deviation (MAD)0.315
Skewness0.191670398
Sum163610.78
Variance0.1783337216
MonotonicityNot monotonic
2022-11-11T11:28:30.771309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
24.005762
11.3%
24.215730
10.8%
24.845708
10.5%
24.11688
10.2%
24.635528
7.8%
23.795521
7.7%
23.69497
7.4%
24.53447
 
6.6%
23.9411
 
6.1%
24.74321
 
4.8%
Other values (7)1123
16.7%
ValueCountFrequency (%)
23.4884
 
1.2%
23.58580
 
1.2%
23.69497
7.4%
23.795521
7.7%
23.9411
6.1%
24.005762
11.3%
24.11688
10.2%
24.215730
10.8%
24.32270
 
4.0%
24.425181
 
2.7%
ValueCountFrequency (%)
25.1693
 
1.4%
25.055150
 
2.2%
24.95265
 
3.9%
24.845708
10.5%
24.74321
4.8%
24.635528
7.8%
24.53447
6.6%
24.425181
 
2.7%
24.32270
 
4.0%
24.215730
10.8%

Z
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct87
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.941494878
Minimum-18.281
Maximum31.0785
Zeros80
Zeros (%)1.2%
Negative768
Negative (%)11.4%
Memory size52.8 KiB
2022-11-11T11:28:30.825130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-18.281
5-th percentile-7.312
Q13.656
median9.75
Q310.969
95-th percentile29.25
Maximum31.0785
Range49.3595
Interquartile range (IQR)7.313

Descriptive statistics

Standard deviation10.98671019
Coefficient of variation (CV)1.105136634
Kurtosis-0.08688786275
Mean9.941494878
Median Absolute Deviation (MAD)6.094
Skewness0.2600738207
Sum66965.9095
Variance120.7078008
MonotonicityNot monotonic
2022-11-11T11:28:30.945823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.751218
18.1%
2.438459
 
6.8%
8.531390
 
5.8%
3.656374
 
5.6%
28.031374
 
5.6%
10.3595339
 
5.0%
26.813298
 
4.4%
9.1405298
 
4.4%
3.047296
 
4.4%
10.969287
 
4.3%
Other values (77)2403
35.7%
ValueCountFrequency (%)
-18.28126
0.4%
-17.67157
 
0.1%
-17.06238
0.6%
-16.4531
 
< 0.1%
-15.84445
0.7%
-15.23452
 
< 0.1%
-14.62535
0.5%
-14.01554
 
0.1%
-13.40635
0.5%
-12.79654
 
0.1%
ValueCountFrequency (%)
31.07855
 
0.1%
30.46939
 
0.6%
29.859575
 
1.1%
29.25270
4.0%
28.6405130
 
1.9%
28.03151
 
< 0.1%
28.031374
5.6%
27.422142
 
2.1%
26.813298
4.4%
26.81254
 
0.1%

Interactions

2022-11-11T11:28:28.777950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.019917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.505534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.063468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.581694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.141240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.628424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.134364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.705224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.180511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.823756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.067756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.554042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.113305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.629533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.187086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.676566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.183289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.750123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.228293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.871647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.117592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.605120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.166203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.680810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.236969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.728336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.299401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.799222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.279385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.923096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.168422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.714751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.220349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.733632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.289110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.781875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.353168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.849054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.332207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.970878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.216641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.764312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.272682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.782548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.336895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.833665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.403956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.896944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.383036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:29.017770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.264359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.813147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.323699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.831034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.384733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.883177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.452892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.943197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.430875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:29.067015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.314903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.864384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.377915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.881901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.434702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.935252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.505974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.993181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.505850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:29.116955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.365731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.916144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.430757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.997477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.485535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.988022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.558105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.042067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.633641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:29.162008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.410278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.964361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.480642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.044318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.531381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.035470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.605628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.087077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.682345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:29.210496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:24.459689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.015189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:25.532858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.094151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:26.580173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.086351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:27.656987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.134915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:28.731180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:28:31.000586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:28:31.074564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:28:31.151305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:28:31.228047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:28:31.297812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-11T11:28:31.354620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:28:29.290089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:28:29.396559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000025.024.824.825.125.125.224.724.825.05523.923.79523.480.000
10.083333025.024.824.825.025.125.224.724.825.05523.923.79523.48-1.219
20.166667025.024.824.825.025.125.224.724.825.05523.923.79523.480.000
30.250000025.024.824.825.025.125.224.724.825.05523.923.79523.48-1.219
40.333333025.024.824.825.025.125.224.724.825.05523.923.79523.48-1.219
50.416667025.024.824.825.025.125.224.724.825.05523.923.79523.48-1.219
60.500000025.024.824.825.025.125.224.724.825.05523.923.69023.48-1.219
70.583333025.024.824.825.025.125.224.724.825.05523.923.79523.48-1.219
80.666667025.024.824.825.025.125.224.724.825.05523.923.69023.48-1.219
90.750000025.024.824.825.025.125.224.724.825.05523.923.69023.48-1.219

Last rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
6726560.500000999825.724.824.825.425.525.224.724.926.73524.32024.42523.96.0940
6727560.583333999825.724.824.825.425.525.224.724.926.73524.32024.42523.96.0940
6728560.666667999825.724.824.825.425.525.224.724.926.73524.32024.42523.96.0940
6729560.750000999825.724.824.825.425.525.224.724.926.73524.42524.42523.96.0940
6730560.833333999825.724.824.825.425.525.224.724.926.73524.42524.42523.96.0940
6731560.916667999825.724.824.825.425.525.224.724.926.73524.42524.42523.96.0940
6732561.000000999825.724.824.825.425.525.224.724.926.73524.42524.42523.96.0940
6733561.083333999825.724.824.825.425.525.224.724.926.73524.42524.42523.96.7035
6734561.166667999825.724.824.825.425.525.224.724.926.73524.42524.42523.97.3130
6735561.250000999825.724.824.825.425.525.224.724.926.73524.42524.42523.96.7035